{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ALLOW_THREADS',\n",
       " 'BUFSIZE',\n",
       " 'CLIP',\n",
       " 'DataSource',\n",
       " 'ERR_CALL',\n",
       " 'ERR_DEFAULT',\n",
       " 'ERR_IGNORE',\n",
       " 'ERR_LOG',\n",
       " 'ERR_PRINT',\n",
       " 'ERR_RAISE',\n",
       " 'ERR_WARN',\n",
       " 'FLOATING_POINT_SUPPORT',\n",
       " 'FPE_DIVIDEBYZERO',\n",
       " 'FPE_INVALID',\n",
       " 'FPE_OVERFLOW',\n",
       " 'FPE_UNDERFLOW',\n",
       " 'False_',\n",
       " 'Inf',\n",
       " 'Infinity',\n",
       " 'MAXDIMS',\n",
       " 'MAY_SHARE_BOUNDS',\n",
       " 'MAY_SHARE_EXACT',\n",
       " 'NAN',\n",
       " 'NINF',\n",
       " 'NZERO',\n",
       " 'NaN',\n",
       " 'PINF',\n",
       " 'PZERO',\n",
       " 'RAISE',\n",
       " 'RankWarning',\n",
       " 'SHIFT_DIVIDEBYZERO',\n",
       " 'SHIFT_INVALID',\n",
       " 'SHIFT_OVERFLOW',\n",
       " 'SHIFT_UNDERFLOW',\n",
       " 'ScalarType',\n",
       " 'True_',\n",
       " 'UFUNC_BUFSIZE_DEFAULT',\n",
       " 'UFUNC_PYVALS_NAME',\n",
       " 'WRAP',\n",
       " '_CopyMode',\n",
       " '_NoValue',\n",
       " '_UFUNC_API',\n",
       " '__NUMPY_SETUP__',\n",
       " '__all__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__config__',\n",
       " '__deprecated_attrs__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__expired_functions__',\n",
       " '__file__',\n",
       " '__former_attrs__',\n",
       " '__future_scalars__',\n",
       " '__getattr__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_add_newdoc_ufunc',\n",
       " '_builtins',\n",
       " '_core',\n",
       " '_distributor_init',\n",
       " '_financial_names',\n",
       " '_get_promotion_state',\n",
       " '_globals',\n",
       " '_int_extended_msg',\n",
       " '_mat',\n",
       " '_no_nep50_warning',\n",
       " '_pyinstaller_hooks_dir',\n",
       " '_pytesttester',\n",
       " '_set_promotion_state',\n",
       " '_specific_msg',\n",
       " '_typing',\n",
       " '_using_numpy2_behavior',\n",
       " '_utils',\n",
       " 'abs',\n",
       " 'absolute',\n",
       " 'add',\n",
       " 'add_docstring',\n",
       " 'add_newdoc',\n",
       " 'add_newdoc_ufunc',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'alltrue',\n",
       " 'amax',\n",
       " 'amin',\n",
       " 'angle',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply_along_axis',\n",
       " 'apply_over_axes',\n",
       " 'arange',\n",
       " 'arccos',\n",
       " 'arccosh',\n",
       " 'arcsin',\n",
       " 'arcsinh',\n",
       " 'arctan',\n",
       " 'arctan2',\n",
       " 'arctanh',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'argwhere',\n",
       " 'around',\n",
       " 'array',\n",
       " 'array2string',\n",
       " 'array_equal',\n",
       " 'array_equiv',\n",
       " 'array_repr',\n",
       " 'array_split',\n",
       " 'array_str',\n",
       " 'asanyarray',\n",
       " 'asarray',\n",
       " 'asarray_chkfinite',\n",
       " 'ascontiguousarray',\n",
       " 'asfarray',\n",
       " 'asfortranarray',\n",
       " 'asmatrix',\n",
       " 'atleast_1d',\n",
       " 'atleast_2d',\n",
       " 'atleast_3d',\n",
       " 'average',\n",
       " 'bartlett',\n",
       " 'base_repr',\n",
       " 'binary_repr',\n",
       " 'bincount',\n",
       " 'bitwise_and',\n",
       " 'bitwise_not',\n",
       " 'bitwise_or',\n",
       " 'bitwise_xor',\n",
       " 'blackman',\n",
       " 'block',\n",
       " 'bmat',\n",
       " 'bool_',\n",
       " 'broadcast',\n",
       " 'broadcast_arrays',\n",
       " 'broadcast_shapes',\n",
       " 'broadcast_to',\n",
       " 'busday_count',\n",
       " 'busday_offset',\n",
       " 'busdaycalendar',\n",
       " 'byte',\n",
       " 'byte_bounds',\n",
       " 'bytes_',\n",
       " 'c_',\n",
       " 'can_cast',\n",
       " 'cast',\n",
       " 'cbrt',\n",
       " 'cdouble',\n",
       " 'ceil',\n",
       " 'cfloat',\n",
       " 'char',\n",
       " 'character',\n",
       " 'chararray',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'clongdouble',\n",
       " 'clongfloat',\n",
       " 'column_stack',\n",
       " 'common_type',\n",
       " 'compare_chararrays',\n",
       " 'compat',\n",
       " 'complex128',\n",
       " 'complex64',\n",
       " 'complex_',\n",
       " 'complexfloating',\n",
       " 'compress',\n",
       " 'concatenate',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'convolve',\n",
       " 'copy',\n",
       " 'copysign',\n",
       " 'copyto',\n",
       " 'corrcoef',\n",
       " 'correlate',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'count_nonzero',\n",
       " 'cov',\n",
       " 'cross',\n",
       " 'csingle',\n",
       " 'ctypeslib',\n",
       " 'cumprod',\n",
       " 'cumproduct',\n",
       " 'cumsum',\n",
       " 'datetime64',\n",
       " 'datetime_as_string',\n",
       " 'datetime_data',\n",
       " 'deg2rad',\n",
       " 'degrees',\n",
       " 'delete',\n",
       " 'deprecate',\n",
       " 'deprecate_with_doc',\n",
       " 'diag',\n",
       " 'diag_indices',\n",
       " 'diag_indices_from',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'diff',\n",
       " 'digitize',\n",
       " 'disp',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dsplit',\n",
       " 'dstack',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'e',\n",
       " 'ediff1d',\n",
       " 'einsum',\n",
       " 'einsum_path',\n",
       " 'emath',\n",
       " 'empty',\n",
       " 'empty_like',\n",
       " 'equal',\n",
       " 'errstate',\n",
       " 'euler_gamma',\n",
       " 'exceptions',\n",
       " 'exp',\n",
       " 'exp2',\n",
       " 'expand_dims',\n",
       " 'expm1',\n",
       " 'expm1x',\n",
       " 'extract',\n",
       " 'eye',\n",
       " 'fabs',\n",
       " 'fastCopyAndTranspose',\n",
       " 'fft',\n",
       " 'fill_diagonal',\n",
       " 'find_common_type',\n",
       " 'finfo',\n",
       " 'fix',\n",
       " 'flatiter',\n",
       " 'flatnonzero',\n",
       " 'flexible',\n",
       " 'flip',\n",
       " 'fliplr',\n",
       " 'flipud',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'float_',\n",
       " 'float_power',\n",
       " 'floating',\n",
       " 'floor',\n",
       " 'floor_divide',\n",
       " 'fmax',\n",
       " 'fmin',\n",
       " 'fmod',\n",
       " 'format_float_positional',\n",
       " 'format_float_scientific',\n",
       " 'format_parser',\n",
       " 'frexp',\n",
       " 'from_dlpack',\n",
       " 'frombuffer',\n",
       " 'fromfile',\n",
       " 'fromfunction',\n",
       " 'fromiter',\n",
       " 'frompyfunc',\n",
       " 'fromregex',\n",
       " 'fromstring',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'gcd',\n",
       " 'generic',\n",
       " 'genfromtxt',\n",
       " 'geomspace',\n",
       " 'get_array_wrap',\n",
       " 'get_include',\n",
       " 'get_printoptions',\n",
       " 'getbufsize',\n",
       " 'geterr',\n",
       " 'geterrcall',\n",
       " 'geterrobj',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'half',\n",
       " 'hamming',\n",
       " 'hanning',\n",
       " 'heaviside',\n",
       " 'histogram',\n",
       " 'histogram2d',\n",
       " 'histogram_bin_edges',\n",
       " 'histogramdd',\n",
       " 'hsplit',\n",
       " 'hstack',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'identity',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'in1d',\n",
       " 'index_exp',\n",
       " 'indices',\n",
       " 'inexact',\n",
       " 'inf',\n",
       " 'info',\n",
       " 'infty',\n",
       " 'inner',\n",
       " 'insert',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_',\n",
       " 'intc',\n",
       " 'integer',\n",
       " 'interp',\n",
       " 'intersect1d',\n",
       " 'intp',\n",
       " 'invert',\n",
       " 'is_busday',\n",
       " 'isclose',\n",
       " 'iscomplex',\n",
       " 'iscomplexobj',\n",
       " 'isfinite',\n",
       " 'isfortran',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isnat',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'isrealobj',\n",
       " 'isscalar',\n",
       " 'issctype',\n",
       " 'issubclass_',\n",
       " 'issubdtype',\n",
       " 'issubsctype',\n",
       " 'iterable',\n",
       " 'ix_',\n",
       " 'kaiser',\n",
       " 'kron',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'left_shift',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lexsort',\n",
       " 'lib',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'little_endian',\n",
       " 'load',\n",
       " 'loadtxt',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logspace',\n",
       " 'longcomplex',\n",
       " 'longdouble',\n",
       " 'longfloat',\n",
       " 'longlong',\n",
       " 'lookfor',\n",
       " 'ma',\n",
       " 'mask_indices',\n",
       " 'mat',\n",
       " 'matmul',\n",
       " 'matrix',\n",
       " 'max',\n",
       " 'maximum',\n",
       " 'maximum_sctype',\n",
       " 'may_share_memory',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memmap',\n",
       " 'meshgrid',\n",
       " 'mgrid',\n",
       " 'min',\n",
       " 'min_scalar_type',\n",
       " 'minimum',\n",
       " 'mintypecode',\n",
       " 'mod',\n",
       " 'modf',\n",
       " 'moveaxis',\n",
       " 'msort',\n",
       " 'multiply',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nanargmax',\n",
       " 'nanargmin',\n",
       " 'nancumprod',\n",
       " 'nancumsum',\n",
       " 'nanmax',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanmin',\n",
       " 'nanpercentile',\n",
       " 'nanprod',\n",
       " 'nanquantile',\n",
       " 'nanstd',\n",
       " 'nansum',\n",
       " 'nanvar',\n",
       " 'nbytes',\n",
       " 'ndarray',\n",
       " 'ndenumerate',\n",
       " 'ndim',\n",
       " 'ndindex',\n",
       " 'nditer',\n",
       " 'negative',\n",
       " 'nested_iters',\n",
       " 'newaxis',\n",
       " 'nextafter',\n",
       " 'nonzero',\n",
       " 'not_equal',\n",
       " 'numarray',\n",
       " 'number',\n",
       " 'obj2sctype',\n",
       " 'object_',\n",
       " 'ogrid',\n",
       " 'oldnumeric',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'outer',\n",
       " 'packbits',\n",
       " 'pad',\n",
       " 'partition',\n",
       " 'percentile',\n",
       " 'pi',\n",
       " 'piecewise',\n",
       " 'place',\n",
       " 'poly',\n",
       " 'poly1d',\n",
       " 'polyadd',\n",
       " 'polyder',\n",
       " 'polydiv',\n",
       " 'polyfit',\n",
       " 'polyint',\n",
       " 'polymul',\n",
       " 'polynomial',\n",
       " 'polysub',\n",
       " 'polyval',\n",
       " 'positive',\n",
       " 'power',\n",
       " 'printoptions',\n",
       " 'prod',\n",
       " 'product',\n",
       " 'promote_types',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'put_along_axis',\n",
       " 'putmask',\n",
       " 'quantile',\n",
       " 'r_',\n",
       " 'rad2deg',\n",
       " 'radians',\n",
       " 'random',\n",
       " 'ravel',\n",
       " 'ravel_multi_index',\n",
       " 'real',\n",
       " 'real_if_close',\n",
       " 'rec',\n",
       " 'recarray',\n",
       " 'recfromcsv',\n",
       " 'recfromtxt',\n",
       " 'reciprocal',\n",
       " 'record',\n",
       " 'remainder',\n",
       " 'repeat',\n",
       " 'require',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'result_type',\n",
       " 'right_shift',\n",
       " 'rint',\n",
       " 'roll',\n",
       " 'rollaxis',\n",
       " 'roots',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'round_',\n",
       " 'row_stack',\n",
       " 's_',\n",
       " 'safe_eval',\n",
       " 'save',\n",
       " 'savetxt',\n",
       " 'savez',\n",
       " 'savez_compressed',\n",
       " 'sctype2char',\n",
       " 'sctypeDict',\n",
       " 'sctypes',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'set_numeric_ops',\n",
       " 'set_printoptions',\n",
       " 'set_string_function',\n",
       " 'setbufsize',\n",
       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'seterrobj',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'show_runtime',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'singlecomplex',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sometrue',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'source',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'str_',\n",
       " 'string_',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'take_along_axis',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'tracemalloc_domain',\n",
       " 'transpose',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'typing',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulonglong',\n",
       " 'unicode_',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unwrap',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vectorize',\n",
       " 'version',\n",
       " 'void',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'where',\n",
       " 'who',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__all__\n",
    "np.__dict__\n",
    "np.__doc__\n",
    "np.__file__\n",
    "#np.__package__\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MailMerge',\n",
       " 'TraderAPI',\n",
       " '__author__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " 'bar',\n",
       " 'bdi',\n",
       " 'broker_tops',\n",
       " 'cap_tops',\n",
       " 'close_apis',\n",
       " 'codecs',\n",
       " 'coins',\n",
       " 'coins_bar',\n",
       " 'coins_snapshot',\n",
       " 'coins_tick',\n",
       " 'coins_trade',\n",
       " 'day_boxoffice',\n",
       " 'day_cinema',\n",
       " 'forecast_data',\n",
       " 'fund',\n",
       " 'fund_holdings',\n",
       " 'futures',\n",
       " 'get_apis',\n",
       " 'get_area_classified',\n",
       " 'get_balance_sheet',\n",
       " 'get_cash_flow',\n",
       " 'get_cashflow_data',\n",
       " 'get_cffex_daily',\n",
       " 'get_concept_classified',\n",
       " 'get_cpi',\n",
       " 'get_czce_daily',\n",
       " 'get_day_all',\n",
       " 'get_dce_daily',\n",
       " 'get_debtpaying_data',\n",
       " 'get_deposit_rate',\n",
       " 'get_fund_info',\n",
       " 'get_future_daily',\n",
       " 'get_gdp_contrib',\n",
       " 'get_gdp_for',\n",
       " 'get_gdp_pull',\n",
       " 'get_gdp_quarter',\n",
       " 'get_gdp_year',\n",
       " 'get_gem_classified',\n",
       " 'get_gold_and_foreign_reserves',\n",
       " 'get_growth_data',\n",
       " 'get_h_data',\n",
       " 'get_hist_data',\n",
       " 'get_hists',\n",
       " 'get_hs300s',\n",
       " 'get_index',\n",
       " 'get_industry_classified',\n",
       " 'get_instrument',\n",
       " 'get_intlfuture',\n",
       " 'get_k_data',\n",
       " 'get_latest_news',\n",
       " 'get_loan_rate',\n",
       " 'get_markets',\n",
       " 'get_money_supply',\n",
       " 'get_money_supply_bal',\n",
       " 'get_nav_close',\n",
       " 'get_nav_grading',\n",
       " 'get_nav_history',\n",
       " 'get_nav_open',\n",
       " 'get_notices',\n",
       " 'get_operation_data',\n",
       " 'get_ppi',\n",
       " 'get_profit_data',\n",
       " 'get_profit_statement',\n",
       " 'get_realtime_quotes',\n",
       " 'get_report_data',\n",
       " 'get_rrr',\n",
       " 'get_shfe_daily',\n",
       " 'get_shfe_vwap',\n",
       " 'get_sina_dd',\n",
       " 'get_sme_classified',\n",
       " 'get_st_classified',\n",
       " 'get_stock_basics',\n",
       " 'get_suspended',\n",
       " 'get_sz50s',\n",
       " 'get_terminated',\n",
       " 'get_tick_data',\n",
       " 'get_today_all',\n",
       " 'get_today_ticks',\n",
       " 'get_token',\n",
       " 'get_zz500s',\n",
       " 'global_realtime',\n",
       " 'guba_sina',\n",
       " 'ht_subs',\n",
       " 'inst_detail',\n",
       " 'inst_tops',\n",
       " 'internet',\n",
       " 'is_holiday',\n",
       " 'latest_content',\n",
       " 'lpr_data',\n",
       " 'lpr_ma_data',\n",
       " 'margin_detail',\n",
       " 'margin_offset',\n",
       " 'margin_target',\n",
       " 'margin_zsl',\n",
       " 'moneyflow_hsgt',\n",
       " 'month_boxoffice',\n",
       " 'new_cbonds',\n",
       " 'new_stocks',\n",
       " 'notice_content',\n",
       " 'os',\n",
       " 'pledged_detail',\n",
       " 'pro',\n",
       " 'pro_api',\n",
       " 'pro_bar',\n",
       " 'profit_data',\n",
       " 'profit_divis',\n",
       " 'quotes',\n",
       " 'realtime_boxoffice',\n",
       " 'realtime_list',\n",
       " 'realtime_quote',\n",
       " 'realtime_tick',\n",
       " 'reset_instrument',\n",
       " 'set_token',\n",
       " 'sh_margin_details',\n",
       " 'sh_margins',\n",
       " 'shibor_data',\n",
       " 'shibor_ma_data',\n",
       " 'shibor_quote_data',\n",
       " 'stock',\n",
       " 'stock_issuance',\n",
       " 'stock_pledged',\n",
       " 'subs',\n",
       " 'sz_margin_details',\n",
       " 'sz_margins',\n",
       " 'tick',\n",
       " 'top10_holders',\n",
       " 'top_list',\n",
       " 'trade_cal',\n",
       " 'trader',\n",
       " 'util',\n",
       " 'xsg_data']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "\n",
    "# 设置 token 并初始化\n",
    "ts.set_token('td1226ad2d0f60bc5f2bc7c95b02eec6f973a46ff62262f772c6d2ef5oken')\n",
    "pro = ts.pro_api()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 初始化pro接口\n",
    "### 在这个网址https://tushare.pro/注册，并在此处获得token，https://tushare.pro/user/token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000002.SZ   20241225   7.71   7.73   7.47   7.54       7.73   -0.19   \n",
      "1     000002.SZ   20241224   7.68   7.73   7.63   7.73       7.66    0.07   \n",
      "2     000002.SZ   20241223   7.79   7.80   7.65   7.66       7.82   -0.16   \n",
      "3     000002.SZ   20241220   7.88   7.93   7.80   7.82       7.91   -0.09   \n",
      "4     000002.SZ   20241219   8.00   8.01   7.82   7.91       8.14   -0.23   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3479  000002.SZ   20100108  10.28  10.38  10.19  10.35      10.28    0.07   \n",
      "3480  000002.SZ   20100107  10.36  10.43  10.24  10.28      10.36   -0.08   \n",
      "3481  000002.SZ   20100106  10.35  10.51  10.20  10.36      10.36    0.00   \n",
      "3482  000002.SZ   20100105  10.51  10.52  10.20  10.36      10.60   -0.24   \n",
      "3483  000002.SZ   20100104  10.85  10.87  10.60  10.60      10.81   -0.21   \n",
      "\n",
      "      pct_chg         vol        amount  \n",
      "0     -2.4580  1517662.88  1.146550e+06  \n",
      "1      0.9138   880607.78  6.778226e+05  \n",
      "2     -2.0460  1169013.30  9.010699e+05  \n",
      "3     -1.1378  1164400.30  9.124935e+05  \n",
      "4     -2.8256  1838122.53  1.451541e+06  \n",
      "...       ...         ...           ...  \n",
      "3479   0.6800  1085304.22  1.117118e+06  \n",
      "3480  -0.7700  1152441.98  1.188788e+06  \n",
      "3481   0.0000  1358604.06  1.405757e+06  \n",
      "3482  -2.2600  1848620.78  1.910014e+06  \n",
      "3483  -1.9400   969832.53  1.034345e+06  \n",
      "\n",
      "[3484 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pro = ts.pro_api('d1226ad2d0f60bc5f2bc7c95b02eec6f973a46ff62262f772c6d2ef5')\n",
    "# 拉取数据\n",
    "df = pro.daily(**{\n",
    "    \"ts_code\": \"000002.SZ\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": 20100101,\n",
    "    \"end_date\": 20241225,\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"close\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000002.SZ   20241225   7.71   7.73   7.47   7.54       7.73   -0.19   \n",
      "1     000002.SZ   20241224   7.68   7.73   7.63   7.73       7.66    0.07   \n",
      "2     000002.SZ   20241223   7.79   7.80   7.65   7.66       7.82   -0.16   \n",
      "3     000002.SZ   20241220   7.88   7.93   7.80   7.82       7.91   -0.09   \n",
      "4     000002.SZ   20241219   8.00   8.01   7.82   7.91       8.14   -0.23   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3479  000002.SZ   20100108  10.28  10.38  10.19  10.35      10.28    0.07   \n",
      "3480  000002.SZ   20100107  10.36  10.43  10.24  10.28      10.36   -0.08   \n",
      "3481  000002.SZ   20100106  10.35  10.51  10.20  10.36      10.36    0.00   \n",
      "3482  000002.SZ   20100105  10.51  10.52  10.20  10.36      10.60   -0.24   \n",
      "3483  000002.SZ   20100104  10.85  10.87  10.60  10.60      10.81   -0.21   \n",
      "\n",
      "      pct_chg         vol        amount  \n",
      "0     -2.4580  1517662.88  1.146550e+06  \n",
      "1      0.9138   880607.78  6.778226e+05  \n",
      "2     -2.0460  1169013.30  9.010699e+05  \n",
      "3     -1.1378  1164400.30  9.124935e+05  \n",
      "4     -2.8256  1838122.53  1.451541e+06  \n",
      "...       ...         ...           ...  \n",
      "3479   0.6800  1085304.22  1.117118e+06  \n",
      "3480  -0.7700  1152441.98  1.188788e+06  \n",
      "3481   0.0000  1358604.06  1.405757e+06  \n",
      "3482  -2.2600  1848620.78  1.910014e+06  \n",
      "3483  -1.9400   969832.53  1.034345e+06  \n",
      "\n",
      "[3484 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"stock000002.csv\")\n",
    "stockname='万科A'\n",
    "stockcode='000002'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3474</th>\n",
       "      <th>3475</th>\n",
       "      <th>3476</th>\n",
       "      <th>3477</th>\n",
       "      <th>3478</th>\n",
       "      <th>3479</th>\n",
       "      <th>3480</th>\n",
       "      <th>3481</th>\n",
       "      <th>3482</th>\n",
       "      <th>3483</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ts_code</th>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>...</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "      <td>000002.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <td>20241225</td>\n",
       "      <td>20241224</td>\n",
       "      <td>20241223</td>\n",
       "      <td>20241220</td>\n",
       "      <td>20241219</td>\n",
       "      <td>20241218</td>\n",
       "      <td>20241217</td>\n",
       "      <td>20241216</td>\n",
       "      <td>20241213</td>\n",
       "      <td>20241212</td>\n",
       "      <td>...</td>\n",
       "      <td>20100115</td>\n",
       "      <td>20100114</td>\n",
       "      <td>20100113</td>\n",
       "      <td>20100112</td>\n",
       "      <td>20100111</td>\n",
       "      <td>20100108</td>\n",
       "      <td>20100107</td>\n",
       "      <td>20100106</td>\n",
       "      <td>20100105</td>\n",
       "      <td>20100104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>7.71</td>\n",
       "      <td>7.68</td>\n",
       "      <td>7.79</td>\n",
       "      <td>7.88</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.17</td>\n",
       "      <td>8.24</td>\n",
       "      <td>8.42</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.63</td>\n",
       "      <td>...</td>\n",
       "      <td>10.06</td>\n",
       "      <td>10.04</td>\n",
       "      <td>9.94</td>\n",
       "      <td>10.13</td>\n",
       "      <td>10.52</td>\n",
       "      <td>10.28</td>\n",
       "      <td>10.36</td>\n",
       "      <td>10.35</td>\n",
       "      <td>10.51</td>\n",
       "      <td>10.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>7.73</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.93</td>\n",
       "      <td>8.01</td>\n",
       "      <td>8.21</td>\n",
       "      <td>8.26</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.61</td>\n",
       "      <td>8.72</td>\n",
       "      <td>...</td>\n",
       "      <td>10.23</td>\n",
       "      <td>10.08</td>\n",
       "      <td>10.08</td>\n",
       "      <td>10.37</td>\n",
       "      <td>10.6</td>\n",
       "      <td>10.38</td>\n",
       "      <td>10.43</td>\n",
       "      <td>10.51</td>\n",
       "      <td>10.52</td>\n",
       "      <td>10.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>7.47</td>\n",
       "      <td>7.63</td>\n",
       "      <td>7.65</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.82</td>\n",
       "      <td>8.13</td>\n",
       "      <td>8.13</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.55</td>\n",
       "      <td>...</td>\n",
       "      <td>10.04</td>\n",
       "      <td>9.91</td>\n",
       "      <td>9.89</td>\n",
       "      <td>9.98</td>\n",
       "      <td>10.08</td>\n",
       "      <td>10.19</td>\n",
       "      <td>10.24</td>\n",
       "      <td>10.2</td>\n",
       "      <td>10.2</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>7.54</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.66</td>\n",
       "      <td>7.82</td>\n",
       "      <td>7.91</td>\n",
       "      <td>8.14</td>\n",
       "      <td>8.14</td>\n",
       "      <td>8.23</td>\n",
       "      <td>8.41</td>\n",
       "      <td>8.69</td>\n",
       "      <td>...</td>\n",
       "      <td>10.14</td>\n",
       "      <td>10.03</td>\n",
       "      <td>10.04</td>\n",
       "      <td>10.29</td>\n",
       "      <td>10.18</td>\n",
       "      <td>10.35</td>\n",
       "      <td>10.28</td>\n",
       "      <td>10.36</td>\n",
       "      <td>10.36</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_close</th>\n",
       "      <td>7.73</td>\n",
       "      <td>7.66</td>\n",
       "      <td>7.82</td>\n",
       "      <td>7.91</td>\n",
       "      <td>8.14</td>\n",
       "      <td>8.14</td>\n",
       "      <td>8.23</td>\n",
       "      <td>8.41</td>\n",
       "      <td>8.69</td>\n",
       "      <td>8.65</td>\n",
       "      <td>...</td>\n",
       "      <td>10.03</td>\n",
       "      <td>10.04</td>\n",
       "      <td>10.29</td>\n",
       "      <td>10.18</td>\n",
       "      <td>10.35</td>\n",
       "      <td>10.28</td>\n",
       "      <td>10.36</td>\n",
       "      <td>10.36</td>\n",
       "      <td>10.6</td>\n",
       "      <td>10.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>-0.19</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>0.04</td>\n",
       "      <td>...</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_chg</th>\n",
       "      <td>-2.458</td>\n",
       "      <td>0.9138</td>\n",
       "      <td>-2.046</td>\n",
       "      <td>-1.1378</td>\n",
       "      <td>-2.8256</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0936</td>\n",
       "      <td>-2.1403</td>\n",
       "      <td>-3.2221</td>\n",
       "      <td>0.4624</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-2.43</td>\n",
       "      <td>1.08</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>0.68</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.26</td>\n",
       "      <td>-1.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>1517662.88</td>\n",
       "      <td>880607.78</td>\n",
       "      <td>1169013.3</td>\n",
       "      <td>1164400.3</td>\n",
       "      <td>1838122.53</td>\n",
       "      <td>695355.05</td>\n",
       "      <td>873037.17</td>\n",
       "      <td>1198130.01</td>\n",
       "      <td>1920781.87</td>\n",
       "      <td>1289042.11</td>\n",
       "      <td>...</td>\n",
       "      <td>1179247.85</td>\n",
       "      <td>1192513.86</td>\n",
       "      <td>1892791.8</td>\n",
       "      <td>1520725.07</td>\n",
       "      <td>1713005.98</td>\n",
       "      <td>1085304.22</td>\n",
       "      <td>1152441.98</td>\n",
       "      <td>1358604.06</td>\n",
       "      <td>1848620.78</td>\n",
       "      <td>969832.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>1146549.84</td>\n",
       "      <td>677822.579</td>\n",
       "      <td>901069.88</td>\n",
       "      <td>912493.458</td>\n",
       "      <td>1451541.257</td>\n",
       "      <td>567471.94</td>\n",
       "      <td>714490.812</td>\n",
       "      <td>994007.98</td>\n",
       "      <td>1624180.476</td>\n",
       "      <td>1114370.152</td>\n",
       "      <td>...</td>\n",
       "      <td>1196520.9477</td>\n",
       "      <td>1192832.6893</td>\n",
       "      <td>1892537.8949</td>\n",
       "      <td>1548907.8618</td>\n",
       "      <td>1758363.7633</td>\n",
       "      <td>1117118.3569</td>\n",
       "      <td>1188788.3687</td>\n",
       "      <td>1405756.5514</td>\n",
       "      <td>1910014.3626</td>\n",
       "      <td>1034344.7604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 3484 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0           1          2           3            4     \\\n",
       "ts_code      000002.SZ   000002.SZ  000002.SZ   000002.SZ    000002.SZ   \n",
       "trade_date    20241225    20241224   20241223    20241220     20241219   \n",
       "open              7.71        7.68       7.79        7.88          8.0   \n",
       "high              7.73        7.73        7.8        7.93         8.01   \n",
       "low               7.47        7.63       7.65         7.8         7.82   \n",
       "close             7.54        7.73       7.66        7.82         7.91   \n",
       "pre_close         7.73        7.66       7.82        7.91         8.14   \n",
       "change           -0.19        0.07      -0.16       -0.09        -0.23   \n",
       "pct_chg         -2.458      0.9138     -2.046     -1.1378      -2.8256   \n",
       "vol         1517662.88   880607.78  1169013.3   1164400.3   1838122.53   \n",
       "amount      1146549.84  677822.579  901069.88  912493.458  1451541.257   \n",
       "\n",
       "                 5           6           7            8            9     ...  \\\n",
       "ts_code     000002.SZ   000002.SZ   000002.SZ    000002.SZ    000002.SZ  ...   \n",
       "trade_date   20241218    20241217    20241216     20241213     20241212  ...   \n",
       "open             8.17        8.24        8.42         8.59         8.63  ...   \n",
       "high             8.21        8.26        8.45         8.61         8.72  ...   \n",
       "low              8.13        8.13         8.2          8.4         8.55  ...   \n",
       "close            8.14        8.14        8.23         8.41         8.69  ...   \n",
       "pre_close        8.14        8.23        8.41         8.69         8.65  ...   \n",
       "change            0.0       -0.09       -0.18        -0.28         0.04  ...   \n",
       "pct_chg           0.0     -1.0936     -2.1403      -3.2221       0.4624  ...   \n",
       "vol         695355.05   873037.17  1198130.01   1920781.87   1289042.11  ...   \n",
       "amount      567471.94  714490.812   994007.98  1624180.476  1114370.152  ...   \n",
       "\n",
       "                    3474          3475          3476          3477  \\\n",
       "ts_code        000002.SZ     000002.SZ     000002.SZ     000002.SZ   \n",
       "trade_date      20100115      20100114      20100113      20100112   \n",
       "open               10.06         10.04          9.94         10.13   \n",
       "high               10.23         10.08         10.08         10.37   \n",
       "low                10.04          9.91          9.89          9.98   \n",
       "close              10.14         10.03         10.04         10.29   \n",
       "pre_close          10.03         10.04         10.29         10.18   \n",
       "change              0.11         -0.01         -0.25          0.11   \n",
       "pct_chg              1.1          -0.1         -2.43          1.08   \n",
       "vol           1179247.85    1192513.86     1892791.8    1520725.07   \n",
       "amount      1196520.9477  1192832.6893  1892537.8949  1548907.8618   \n",
       "\n",
       "                    3478          3479          3480          3481  \\\n",
       "ts_code        000002.SZ     000002.SZ     000002.SZ     000002.SZ   \n",
       "trade_date      20100111      20100108      20100107      20100106   \n",
       "open               10.52         10.28         10.36         10.35   \n",
       "high                10.6         10.38         10.43         10.51   \n",
       "low                10.08         10.19         10.24          10.2   \n",
       "close              10.18         10.35         10.28         10.36   \n",
       "pre_close          10.35         10.28         10.36         10.36   \n",
       "change             -0.17          0.07         -0.08           0.0   \n",
       "pct_chg            -1.64          0.68         -0.77           0.0   \n",
       "vol           1713005.98    1085304.22    1152441.98    1358604.06   \n",
       "amount      1758363.7633  1117118.3569  1188788.3687  1405756.5514   \n",
       "\n",
       "                    3482          3483  \n",
       "ts_code        000002.SZ     000002.SZ  \n",
       "trade_date      20100105      20100104  \n",
       "open               10.51         10.85  \n",
       "high               10.52         10.87  \n",
       "low                 10.2          10.6  \n",
       "close              10.36          10.6  \n",
       "pre_close           10.6         10.81  \n",
       "change             -0.24         -0.21  \n",
       "pct_chg            -2.26         -1.94  \n",
       "vol           1848620.78     969832.53  \n",
       "amount      1910014.3626  1034344.7604  \n",
       "\n",
       "[11 rows x 3484 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x188b4b96630>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据\n",
    "#coding=utf-8\n",
    "#使用pandas读取数据\n",
    "mydata=pd.read_csv(\"./stock000002.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "plt.plot(mydata[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3454 entries, 0 to 3453\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3454 non-null   datetime64[ns]\n",
      " 1   open        3454 non-null   float64       \n",
      " 2   high        3454 non-null   float64       \n",
      " 3   low         3454 non-null   float64       \n",
      " 4   close       3454 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 135.1 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3449</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.01</td>\n",
       "      <td>7.82</td>\n",
       "      <td>7.91</td>\n",
       "      <td>8.166</td>\n",
       "      <td>8.385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3450</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>7.88</td>\n",
       "      <td>7.93</td>\n",
       "      <td>7.80</td>\n",
       "      <td>7.82</td>\n",
       "      <td>8.048</td>\n",
       "      <td>8.299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3451</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>7.79</td>\n",
       "      <td>7.80</td>\n",
       "      <td>7.65</td>\n",
       "      <td>7.66</td>\n",
       "      <td>7.934</td>\n",
       "      <td>8.223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3452</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>7.68</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.63</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.852</td>\n",
       "      <td>8.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3453</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>7.71</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.47</td>\n",
       "      <td>7.54</td>\n",
       "      <td>7.732</td>\n",
       "      <td>8.027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date  open  high   low  close      5     10\n",
       "3449 2024-12-19  8.00  8.01  7.82   7.91  8.166  8.385\n",
       "3450 2024-12-20  7.88  7.93  7.80   7.82  8.048  8.299\n",
       "3451 2024-12-23  7.79  7.80  7.65   7.66  7.934  8.223\n",
       "3452 2024-12-24  7.68  7.73  7.63   7.73  7.852  8.138\n",
       "3453 2024-12-25  7.71  7.73  7.47   7.54  7.732  8.027"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "# import akshare as ak\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname, fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date   open   high    low  close\n",
      "0    2010-01-04  10.85  10.87  10.60  10.60\n",
      "1    2010-01-05  10.51  10.52  10.20  10.36\n",
      "2    2010-01-06  10.35  10.51  10.20  10.36\n",
      "3    2010-01-07  10.36  10.43  10.24  10.28\n",
      "4    2010-01-08  10.28  10.38  10.19  10.35\n",
      "...         ...    ...    ...    ...    ...\n",
      "3479 2024-12-19   8.00   8.01   7.82   7.91\n",
      "3480 2024-12-20   7.88   7.93   7.80   7.82\n",
      "3481 2024-12-23   7.79   7.80   7.65   7.66\n",
      "3482 2024-12-24   7.68   7.73   7.63   7.73\n",
      "3483 2024-12-25   7.71   7.73   7.47   7.54\n",
      "\n",
      "[3484 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3484 entries, 0 to 3483\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3484 non-null   datetime64[ns]\n",
      " 1   open        3484 non-null   float64       \n",
      " 2   high        3484 non-null   float64       \n",
      " 3   low         3484 non-null   float64       \n",
      " 4   close       3484 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 136.2 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>10.85</td>\n",
       "      <td>10.87</td>\n",
       "      <td>10.60</td>\n",
       "      <td>10.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>10.51</td>\n",
       "      <td>10.52</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>10.35</td>\n",
       "      <td>10.51</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>10.36</td>\n",
       "      <td>10.43</td>\n",
       "      <td>10.24</td>\n",
       "      <td>10.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>10.28</td>\n",
       "      <td>10.38</td>\n",
       "      <td>10.19</td>\n",
       "      <td>10.35</td>\n",
       "      <td>10.390</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3479</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.01</td>\n",
       "      <td>7.82</td>\n",
       "      <td>7.91</td>\n",
       "      <td>8.166</td>\n",
       "      <td>8.385</td>\n",
       "      <td>8.626333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3480</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>7.88</td>\n",
       "      <td>7.93</td>\n",
       "      <td>7.80</td>\n",
       "      <td>7.82</td>\n",
       "      <td>8.048</td>\n",
       "      <td>8.299</td>\n",
       "      <td>8.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3481</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>7.79</td>\n",
       "      <td>7.80</td>\n",
       "      <td>7.65</td>\n",
       "      <td>7.66</td>\n",
       "      <td>7.934</td>\n",
       "      <td>8.223</td>\n",
       "      <td>8.515333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3482</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>7.68</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.63</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.852</td>\n",
       "      <td>8.138</td>\n",
       "      <td>8.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3483</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>7.71</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.47</td>\n",
       "      <td>7.54</td>\n",
       "      <td>7.732</td>\n",
       "      <td>8.027</td>\n",
       "      <td>8.415667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3484 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5     10        30\n",
       "0    2010-01-04  10.85  10.87  10.60  10.60     NaN    NaN       NaN\n",
       "1    2010-01-05  10.51  10.52  10.20  10.36     NaN    NaN       NaN\n",
       "2    2010-01-06  10.35  10.51  10.20  10.36     NaN    NaN       NaN\n",
       "3    2010-01-07  10.36  10.43  10.24  10.28     NaN    NaN       NaN\n",
       "4    2010-01-08  10.28  10.38  10.19  10.35  10.390    NaN       NaN\n",
       "...         ...    ...    ...    ...    ...     ...    ...       ...\n",
       "3479 2024-12-19   8.00   8.01   7.82   7.91   8.166  8.385  8.626333\n",
       "3480 2024-12-20   7.88   7.93   7.80   7.82   8.048  8.299  8.570000\n",
       "3481 2024-12-23   7.79   7.80   7.65   7.66   7.934  8.223  8.515333\n",
       "3482 2024-12-24   7.68   7.73   7.63   7.73   7.852  8.138  8.466667\n",
       "3483 2024-12-25   7.71   7.73   7.47   7.54   7.732  8.027  8.415667\n",
       "\n",
       "[3484 rows x 8 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname+\"股价走势\", fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(10)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.items of       trade_date  close\n",
       "0           7.71   7.73\n",
       "1           7.68   7.73\n",
       "2           7.79   7.80\n",
       "3           7.88   7.93\n",
       "4           8.00   8.01\n",
       "...          ...    ...\n",
       "3479       10.28  10.38\n",
       "3480       10.36  10.43\n",
       "3481       10.35  10.51\n",
       "3482       10.51  10.52\n",
       "3483       10.85  10.87\n",
       "\n",
       "[3484 rows x 2 columns]>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10.6 ],\n",
       "       [10.36],\n",
       "       [10.36],\n",
       "       ...,\n",
       "       [ 7.66],\n",
       "       [ 7.73],\n",
       "       [ 7.54]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,2677)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14613.0</th>\n",
       "      <td>10.85</td>\n",
       "      <td>10.87</td>\n",
       "      <td>10.60</td>\n",
       "      <td>10.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14614.0</th>\n",
       "      <td>10.51</td>\n",
       "      <td>10.52</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14615.0</th>\n",
       "      <td>10.35</td>\n",
       "      <td>10.51</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14616.0</th>\n",
       "      <td>10.36</td>\n",
       "      <td>10.43</td>\n",
       "      <td>10.24</td>\n",
       "      <td>10.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14617.0</th>\n",
       "      <td>10.28</td>\n",
       "      <td>10.38</td>\n",
       "      <td>10.19</td>\n",
       "      <td>10.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20076.0</th>\n",
       "      <td>8.00</td>\n",
       "      <td>8.01</td>\n",
       "      <td>7.82</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20077.0</th>\n",
       "      <td>7.88</td>\n",
       "      <td>7.93</td>\n",
       "      <td>7.80</td>\n",
       "      <td>7.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20080.0</th>\n",
       "      <td>7.79</td>\n",
       "      <td>7.80</td>\n",
       "      <td>7.65</td>\n",
       "      <td>7.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20081.0</th>\n",
       "      <td>7.68</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.63</td>\n",
       "      <td>7.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20082.0</th>\n",
       "      <td>7.71</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.47</td>\n",
       "      <td>7.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3484 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          open   high    low  close\n",
       "date                               \n",
       "14613.0  10.85  10.87  10.60  10.60\n",
       "14614.0  10.51  10.52  10.20  10.36\n",
       "14615.0  10.35  10.51  10.20  10.36\n",
       "14616.0  10.36  10.43  10.24  10.28\n",
       "14617.0  10.28  10.38  10.19  10.35\n",
       "...        ...    ...    ...    ...\n",
       "20076.0   8.00   8.01   7.82   7.91\n",
       "20077.0   7.88   7.93   7.80   7.82\n",
       "20080.0   7.79   7.80   7.65   7.66\n",
       "20081.0   7.68   7.73   7.63   7.73\n",
       "20082.0   7.71   7.73   7.47   7.54\n",
       "\n",
       "[3484 rows x 4 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14613.0</td>\n",
       "      <td>10.85</td>\n",
       "      <td>10.87</td>\n",
       "      <td>10.60</td>\n",
       "      <td>10.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14614.0</td>\n",
       "      <td>10.51</td>\n",
       "      <td>10.52</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14615.0</td>\n",
       "      <td>10.35</td>\n",
       "      <td>10.51</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14616.0</td>\n",
       "      <td>10.36</td>\n",
       "      <td>10.43</td>\n",
       "      <td>10.24</td>\n",
       "      <td>10.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14617.0</td>\n",
       "      <td>10.28</td>\n",
       "      <td>10.38</td>\n",
       "      <td>10.19</td>\n",
       "      <td>10.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3479</th>\n",
       "      <td>20076.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.01</td>\n",
       "      <td>7.82</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3480</th>\n",
       "      <td>20077.0</td>\n",
       "      <td>7.88</td>\n",
       "      <td>7.93</td>\n",
       "      <td>7.80</td>\n",
       "      <td>7.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3481</th>\n",
       "      <td>20080.0</td>\n",
       "      <td>7.79</td>\n",
       "      <td>7.80</td>\n",
       "      <td>7.65</td>\n",
       "      <td>7.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3482</th>\n",
       "      <td>20081.0</td>\n",
       "      <td>7.68</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.63</td>\n",
       "      <td>7.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3483</th>\n",
       "      <td>20082.0</td>\n",
       "      <td>7.71</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.47</td>\n",
       "      <td>7.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3484 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close\n",
       "0     14613.0  10.85  10.87  10.60  10.60\n",
       "1     14614.0  10.51  10.52  10.20  10.36\n",
       "2     14615.0  10.35  10.51  10.20  10.36\n",
       "3     14616.0  10.36  10.43  10.24  10.28\n",
       "4     14617.0  10.28  10.38  10.19  10.35\n",
       "...       ...    ...    ...    ...    ...\n",
       "3479  20076.0   8.00   8.01   7.82   7.91\n",
       "3480  20077.0   7.88   7.93   7.80   7.82\n",
       "3481  20080.0   7.79   7.80   7.65   7.66\n",
       "3482  20081.0   7.68   7.73   7.63   7.73\n",
       "3483  20082.0   7.71   7.73   7.47   7.54\n",
       "\n",
       "[3484 rows x 5 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x188bb6a9580>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:2048, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[3626 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\76496\\AppData\\Roaming\\Python\\Python312\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - loss: 0.0809 - val_loss: 0.0241\n",
      "Epoch 2/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0101 - val_loss: 3.9316e-04\n",
      "Epoch 3/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0010 - val_loss: 2.9600e-04\n",
      "Epoch 4/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.0883e-04 - val_loss: 2.4310e-04\n",
      "Epoch 5/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.7487e-04 - val_loss: 2.3732e-04\n",
      "Epoch 6/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.7070e-04 - val_loss: 2.3533e-04\n",
      "Epoch 7/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.6723e-04 - val_loss: 2.3544e-04\n",
      "Epoch 8/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.6393e-04 - val_loss: 2.3899e-04\n",
      "Epoch 9/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.6090e-04 - val_loss: 2.3822e-04\n",
      "Epoch 10/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.5660e-04 - val_loss: 2.3525e-04\n",
      "Epoch 11/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.5271e-04 - val_loss: 2.3024e-04\n",
      "Epoch 12/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.4921e-04 - val_loss: 2.2348e-04\n",
      "Epoch 13/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.4633e-04 - val_loss: 2.2159e-04\n",
      "Epoch 14/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.4447e-04 - val_loss: 2.1968e-04\n",
      "Epoch 15/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4.4245e-04 - val_loss: 2.1987e-04\n",
      "Epoch 16/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.4061e-04 - val_loss: 2.2014e-04\n",
      "Epoch 17/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.3915e-04 - val_loss: 2.1995e-04\n",
      "Epoch 18/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.3802e-04 - val_loss: 2.1979e-04\n",
      "Epoch 19/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.3624e-04 - val_loss: 2.2115e-04\n",
      "Epoch 20/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.3495e-04 - val_loss: 2.2101e-04\n",
      "Epoch 21/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.3463e-04 - val_loss: 2.1857e-04\n",
      "Epoch 22/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.3190e-04 - val_loss: 2.1781e-04\n",
      "Epoch 23/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.2864e-04 - val_loss: 2.1712e-04\n",
      "Epoch 24/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.2536e-04 - val_loss: 2.1750e-04\n",
      "Epoch 25/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.2310e-04 - val_loss: 2.1757e-04\n",
      "Epoch 26/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.2113e-04 - val_loss: 2.1691e-04\n",
      "Epoch 27/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.1881e-04 - val_loss: 2.1667e-04\n",
      "Epoch 28/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.1649e-04 - val_loss: 2.1641e-04\n",
      "Epoch 29/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.1425e-04 - val_loss: 2.1622e-04\n",
      "Epoch 30/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.1207e-04 - val_loss: 2.1586e-04\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">91,955</span> (359.20 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m91,955\u001b[0m (359.20 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">61,304</span> (239.47 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m61,304\u001b[0m (239.47 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title(stockname)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 0.25782\n",
      "均方根误差: 0.50776\n",
      "平均绝对误差: 0.39685\n",
      "R2: 0.71473\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
